{"id":"W1980225370","doi":"10.1152/japplphysiol.01251.2011","title":"A biomechanical model for encoding joint dynamics: applications to transfemoral prosthesis control","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Physiology","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Canadian Institutes of Health Research; Össur","keywords":"Kinematics; Energy (signal processing); Dissipation; Computer science; Joint (building); Gait; Torque; Control theory (sociology); Simulation; Knee Joint; Mechanical energy; Encoding (memory); Efficient energy use; Engineering; Physical medicine and rehabilitation; Artificial intelligence; Control (management); Mathematics; Physics; Structural engineering; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004692231,0.0005700521,0.0004574145,0.000474155,0.0002961979,0.0005548931,0.0008299196,0.00069584,0.001913612],"category_scores_gemma":[0.001512221,0.0002662831,0.0004507667,0.0003677115,0.0005077043,0.0006513796,0.0003333897,0.0005894276,0.0003844714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004332572,"about_ca_system_score_gemma":0.0006605085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006058288,"about_ca_topic_score_gemma":0.005671903,"domain_scores_codex":[0.9998614,0.00003321854,0.00001432027,0.00003429702,0.0000471893,0.000009603139],"domain_scores_gemma":[0.9997401,0.0001278366,0.00003874049,0.00002907806,0.0000505914,0.00001362074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005792924,0.00004703949,0.0005755372,0.00008382113,0.00002447702,0.00009012099,0.00007333047,0.8887404,0.01231482,0.022057,0.0004030316,0.07553256],"study_design_scores_gemma":[0.000003857512,0.00003353967,0.000168123,0.000006888968,0.000003759459,0.00002467693,0.000004040869,0.9949625,0.000444879,0.003743283,0.0005974504,0.000006983394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00625134,0.000161784,0.992347,0.000148731,0.00003786245,0.00002150167,0.00004281949,0.000204154,0.0007848032],"genre_scores_gemma":[0.5546005,0.0009134904,0.4392747,0.0001086537,0.00010327,0.0003308353,0.0001691143,0.0001013171,0.00439808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006058288,"threshold_uncertainty_score":0.01204604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811408288069676,"score_gpt":0.2428027402315336,"score_spread":0.2246886573508368,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}